Datasets:
Formats:
parquet
Sub-tasks:
tabular-multi-class-classification
Languages:
English
Size:
< 1K
ArXiv:
Tags:
continuous-glucose-monitor
cgm
insulin-resistance
beta-cell-dysfunction
metabolic-subphenotype
ogtt
License:
Add paper link, GitHub repository, and update metadata
Browse filesThis PR updates the dataset card to include:
- A link to the paper: [CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining](https://huggingface.co/papers/2605.00933).
- A link to the official GitHub repository.
- Improved metadata including the `time-series-forecasting` task category.
- Updated citation information with the official BibTeX.
README.md
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---
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license: mit
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language:
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- en
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-
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task_categories:
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- tabular-classification
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- feature-extraction
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task_ids:
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- tabular-multi-class-classification
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modalities:
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- Time Series
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- Tabular
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- healthcare
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- time-series
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- subject-level-classification
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size_categories:
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- n<1K
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configs:
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- config_name: default
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data_files:
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# CGM-JEPA Downstream Evaluation Splits
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> Downstream-only. For the unlabeled pretraining corpus (Stanford + Colas), see [`CRUISEResearchGroup/CGM-JEPA-Pretraining`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Pretraining). For pretrained encoder weights, see [`CRUISEResearchGroup/CGM-JEPA`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).
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## Citation
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## Code repository
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[
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---
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language:
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- en
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license: mit
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size_categories:
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- n<1K
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task_categories:
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- time-series-forecasting
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- tabular-classification
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- feature-extraction
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task_ids:
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- tabular-multi-class-classification
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pretty_name: CGM-JEPA Downstream Evaluation Splits
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modalities:
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- Time Series
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- Tabular
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- healthcare
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- time-series
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- subject-level-classification
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configs:
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- config_name: default
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data_files:
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# CGM-JEPA Downstream Evaluation Splits
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[Paper](https://huggingface.co/papers/2605.00933) | [Code](https://github.com/cruiseresearchgroup/CGM-JEPA)
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Labeled cohort splits used to evaluate CGM encoders on two binary metabolic outcomes — **insulin resistance** and **β-cell dysfunction** — in the paper [CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining](https://huggingface.co/papers/2605.00933).
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> Downstream-only. For the unlabeled pretraining corpus (Stanford + Colas), see [`CRUISEResearchGroup/CGM-JEPA-Pretraining`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Pretraining). For pretrained encoder weights, see [`CRUISEResearchGroup/CGM-JEPA`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).
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## Citation
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```bibtex
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@article{muhammad2026cgm,
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title = {CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining},
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author = {Muhammad, Hada Melino and Li, Zechen and Salim, Flora and Metwally, Ahmed A},
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journal = {arXiv preprint arXiv:2605.00933},
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year = {2026}
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}
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```
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## Code repository
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[https://github.com/cruiseresearchgroup/CGM-JEPA](https://github.com/cruiseresearchgroup/CGM-JEPA)
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